Deep neural networks achieve state-of-the-art results for accelerated MRI reconstruction. Most research on deep learning based imaging focuses on improving neural network architectures trained and evaluated on fixed and homogeneous training and evaluation data. In this work, we investigate data curation strategies for improving MRI reconstruction. We assemble a large dataset of raw k-space data from 18 public sources consisting of 1.1M images and construct a diverse evaluation set comprising 48 test sets, capturing variations in anatomy, contrast, number of coils, and other key factors. We propose and study different data filtering strategies to enhance performance of current state-of-the-art neural networks for accelerated MRI reconstruction. Our experiments show that filtering the training data leads to consistent, albeit modest, performance gains. These performance gains are robust across different training set sizes and accelerations, and we find that filtering is particularly beneficial when the proportion of in-distribution data in the unfiltered training set is low.

In recent years, point-based methods have achieved promising performance on 3D object detection task. Although effective, they still suffer from the inherent sparsity of point cloud, which makes it challenging to distinguish objects with backgrounds only relying on the view of raw point. To this end, we propose a straightforward yet effective multi-view fusion network termed RAFDet to alleviate this issue. The core idea of our method lies in combining the merits of raw point and its range view to enhance the representation learning for sparse point cloud, thus mitigating the sparsity problem and boosting the detection performance. In particular, we introduce a novel bidirectional attentive fusion module to equip sparse point with interacted fine-grained semantic clues during feature learning process. Then, we devise the range-view augmented fusion module to fully exploit the supplementary relationship between different perspectives with the aim of enhancing original point-view features. In the end, a single-stage detection head is utilized to predict final 3D bounding boxes based on the enhanced semantics. We have evaluated our method on the popular KITTI Dataset, DAIR-V2X Dataset and Waymo Open Dataset. Experimental results on the above three datasets demonstrate the effectiveness and robustness of our approach in terms of detection performance and model complexity.

The purpose of weakly-supervised temporal action localization (WTAL) task is to simultaneously classify and localize action instances in untrimmed videos with only video-level labels. Previous works fail to extract multi-scale temporal features to identify action instances with different durations, and they do not fully use the temporal cues of action video to learn discriminative features. In addition, the classifiers trained by current methods usually focus on easy-to-distinguish snippets while ignoring other semantically ambiguous features, which leads to incomplete and over-complete localization. To address these issues, we introduce a new Snippet-inter Difference Attention Network (SDANet) for WTAL, which can be trained end-to-end. Specifically, our model presents three modules, with primary contributions lying in the snippet-inter difference attention (SDA) module and potential feature mining (PFM) module. Firstly, we construct a simple multi-scale temporal feature fusion (MTFF) module to generate multi-scale temporal feature representation, so as to help the model better detect short action instances. Secondly, we consider the temporal cues of video features and design SDA module based on the Transformer to capture global discriminative features for each modality based on multi-scale features. It calculates the differences between temporal neighbor snippets in each modality to explore salient-difference features, and then utilizes them to guide correlation modeling. Thirdly, after learning discriminative features, we devise PFM module to excavate potential action and background snippets from ambiguous features. By contrastive learning, potential actions are forced closer to discriminative actions and away from the background, thereby learning more accurate action boundaries. Finally, two losses (i.e., similarity loss and reconstruction loss) are further developed to constrain the consistency between two modalities and help the model retain original feature information for better localization results. Extensive experiments show that our model achieves better performance against current WTAL methods on three datasets, i.e., THUMOS14, ActivityNet1.2 and ActivityNet1.3.

Weakly-Supervised Temporal Action Localization (WTAL) aims to identify the temporal boundaries and classify actions in untrimmed videos using only video-level labels during training. Despite recent progress, many existing approaches primarily follow a localization-by-classification pipeline, treating snippets as independent instances and thus exploiting only limited contextual information. Besides, these methods struggle to capture multi-scale temporal information and neglect both the internal temporal structures within videos and the semantic consistency between videos, resulting in misclassification and inaccurate localization. To address these limitations, we introduce a novel Temporal and Semantic Correlation Network (TSC-Net) for WTAL task, which can be trained end-to-end. First, we propose a Multi-Scale Features Integration Pyramid (MFIP) module to integrate multi-scale temporal features, effectively addressing the challenge of missed detections caused by short action durations. Furthermore, we design a Temporal Correlation Enhancement (TCE) branch to enhance segment correlations by video-level temporal structures to improve the completeness of action localization. Finally, a Dataset-Wide Semantic Awareness (DSA) branch is designed to construct and propagate a dataset-level action semantics bank, enhancing the model’s awareness of semantic consistency in actions. Extensive experiments show that TSC-Net outperforms most existing WTAL methods, achieving an average mAP of 46.3% on the THUMOS-14 dataset and 26.5% on the ActivityNet1.2 dataset. Detailed ablation studies further confirm the effectiveness of each component in our model. The code and models are publicly available at https://github.com/linkang-els/TSC-Net-main .

Deep learning based methods for image reconstruction are state-of-the-art for a variety of imaging tasks. However, neural networks often perform worse if the training data differs significantly from the data they are applied to. For example, a model trained for accelerated magnetic resonance imaging (MRI) on one scanner performs worse on another scanner. In this work, we investigate the impact of the training data on a model's performance and robustness for accelerated MRI. We find that models trained on the combination of various data distributions, such as those obtained from different MRI scanners and anatomies, exhibit robustness equal or superior to models trained on the best single distribution for a specific target distribution. Thus training on such diverse data tends to improve robustness. Furthermore, training on such a diverse dataset does not compromise in-distribution performance, i.e., a model trained on diverse data yields in-distribution performance at least as good as models trained on the more narrow individual distributions. Our results suggest that training a model for imaging on a variety of distributions tends to yield a more effective and robust model than maintaining separate models for individual distributions.

Automatic seizure identification plays an important role in epilepsy evaluation. Most existing methods regard seizure identification as a classification problem and rely on labelled training set. However, labelling seizure onset is very expensive and seizure data for each individual is especially limited, classifier-based methods are usually impractical in use. Clustering methods could learn useful information from unlabelled data, while they may lead to unstable results given epileptic signals with high noises. In this paper, we propose to use Gaussian temporal-constrained k-medoids method for seizure state segmentation. Using temporal information, the noises could be effectively suppressed and robust clustering performance is achieved. Besides, a new criterion called signed total variation (STV) which describes temporal integrity and consistency is proposed for temporal-constrained clustering evaluation. Experimental results show that, compared with the existing methods, the k-medoids method with Gaussian temporal constraint achieves the best results on both F1-score and STV.